mirror of https://github.com/explosion/spaCy.git
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ..util import ensure_path
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from ..util import model_to_bytes, model_from_bytes
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from .. import util
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from ..displacy import parse_deps, parse_ents
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from ..tokens import Span
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from .util import get_doc
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from pathlib import Path
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import pytest
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from thinc.neural import Maxout, Softmax
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from thinc.api import chain
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@pytest.mark.parametrize('text', ['hello/world', 'hello world'])
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def test_util_ensure_path_succeeds(text):
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path = util.ensure_path(text)
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assert isinstance(path, Path)
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def test_simple_model_roundtrip_bytes():
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model = Maxout(5, 10, pieces=2)
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model.b += 1
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data = model_to_bytes(model)
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model.b -= 1
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model_from_bytes(model, data)
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assert model.b[0, 0] == 1
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def test_multi_model_roundtrip_bytes():
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model = chain(Maxout(5, 10, pieces=2), Maxout(2, 3))
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model._layers[0].b += 1
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model._layers[1].b += 2
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data = model_to_bytes(model)
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model._layers[0].b -= 1
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model._layers[1].b -= 2
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model_from_bytes(model, data)
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assert model._layers[0].b[0, 0] == 1
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assert model._layers[1].b[0, 0] == 2
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def test_multi_model_load_missing_dims():
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model = chain(Maxout(5, 10, pieces=2), Maxout(2, 3))
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model._layers[0].b += 1
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model._layers[1].b += 2
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data = model_to_bytes(model)
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model2 = chain(Maxout(5), Maxout())
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model_from_bytes(model2, data)
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assert model2._layers[0].b[0, 0] == 1
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assert model2._layers[1].b[0, 0] == 2
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@pytest.mark.parametrize('package', ['thinc'])
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def test_util_is_package(package):
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"""Test that an installed package via pip is recognised by util.is_package."""
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assert util.is_package(package)
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@pytest.mark.parametrize('package', ['thinc'])
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def test_util_get_package_path(package):
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"""Test that a Path object is returned for a package name."""
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path = util.get_package_path(package)
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assert isinstance(path, Path)
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def test_displacy_parse_ents(en_vocab):
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"""Test that named entities on a Doc are converted into displaCy's format."""
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doc = get_doc(en_vocab, words=["But", "Google", "is", "starting", "from", "behind"])
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doc.ents = [Span(doc, 1, 2, label=doc.vocab.strings[u'ORG'])]
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ents = parse_ents(doc)
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assert isinstance(ents, dict)
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assert ents['text'] == 'But Google is starting from behind '
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assert ents['ents'] == [{'start': 4, 'end': 10, 'label': 'ORG'}]
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def test_displacy_parse_deps(en_vocab):
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"""Test that deps and tags on a Doc are converted into displaCy's format."""
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words = ["This", "is", "a", "sentence"]
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heads = [1, 0, 1, -2]
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tags = ['DT', 'VBZ', 'DT', 'NN']
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deps = ['nsubj', 'ROOT', 'det', 'attr']
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doc = get_doc(en_vocab, words=words, heads=heads, tags=tags, deps=deps)
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deps = parse_deps(doc)
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assert isinstance(deps, dict)
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assert deps['words'] == [{'text': 'This', 'tag': 'DT'},
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{'text': 'is', 'tag': 'VBZ'},
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{'text': 'a', 'tag': 'DT'},
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{'text': 'sentence', 'tag': 'NN'}]
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assert deps['arcs'] == [{'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
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{'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
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{'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
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